
versioninfo()

Julia Version 1.10.0
Commit 3120989f39b (2023-12-25 18:01 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: macOS (arm64-apple-darwin22.4.0)
  CPU: 12 × Apple M2 Pro
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, apple-m1)
  Threads: 1 on 8 virtual cores
Environment:
  JULIA_NUM_THREADS =


import Pkg
Pkg.activate()
Pkg.status()

  Activating project at `~/.julia/environments/v1.10`

Status `~/.julia/environments/v1.10/Project.toml`
  [cbdf2221] AlgebraOfGraphics v0.6.18
  [024491cd] BetaML v0.11.4
⌃ [336ed68f] CSV v0.10.12
  [13f3f980] CairoMakie v0.11.9
  [e2e10f9a] CatBoost v0.3.4
  [324d7699] CategoricalArrays v0.10.8
  [aaaa29a8] Clustering v0.15.7
  [8f4d0f93] Conda v1.10.0
  [a93c6f00] DataFrames v1.6.1
⌃ [1313f7d8] DataFramesMeta v0.14.1
  [b4f34e82] Distances v0.10.11
  [31c24e10] Distributions v0.25.107
⌃ [5789e2e9] FileIO v1.16.2
⌃ [587475ba] Flux v0.14.12
  [da1fdf0e] FreqTables v0.4.6
  [38e38edf] GLM v1.9.0
  [8d5ece8b] GLMNet v0.7.2
  [e9467ef8] GLMakie v0.9.9
  [09f84164] HypothesisTests v0.11.0
  [7073ff75] IJulia v1.24.2
  [4e3cecfd] ImageShow v0.3.8
  [f0e99cf1] MLBase v0.9.2
  [eb30cadb] MLDatasets v0.7.14
⌃ [add582a8] MLJ v0.20.2
  [d354fa79] MLJClusteringInterface v0.1.11
  [c6f25543] MLJDecisionTreeInterface v0.4.1
  [094fc8d1] MLJFlux v0.4.0
  [caf8df21] MLJGLMInterface v0.3.7
  [61c7150f] MLJLIBSVMInterface v0.2.1
  [6ee0df7b] MLJLinearModels v0.10.0
⌃ [d491faf4] MLJModels v0.16.15
  [1b6a4a23] MLJMultivariateStatsInterface v0.5.3
  [33e4bacb] MLJNaiveBayesInterface v0.1.6
  [5ae90465] MLJScikitLearnInterface v0.6.1
  [636a865e] NearestNeighborModels v0.2.3
  [8b842266] PalmerPenguins v0.1.4
⌃ [91a5bcdd] Plots v1.40.1
  [8162dcfd] PrettyPrint v0.2.0
  [ce6b1742] RDatasets v0.7.7
  [321657f4] ScientificTypes v3.0.2
  [8e980c4a] Shapefile v0.12.0
  [de6bee2f] SimpleChains v0.4.6
  [860ef19b] StableRNGs v1.0.1
  [2913bbd2] StatsBase v0.34.2
  [f3b207a7] StatsPlots v0.15.7
  [40c74d1a] TableView v0.7.2
  [fdbf4ff8] XLSX v0.10.1
  [37e2e46d] LinearAlgebra
  [9a3f8284] Random
Info Packages marked with ⌃ have new versions available and may be upgradable.


# Loading Necessary Packages
using CSV, DataFrames, Shapefile
using CategoricalArrays, FreqTables
using Plots, StatsPlots, Statistics
using LinearAlgebra, StatsBase, HypothesisTests
using Distributions, Random, StableRNGs
using PalmerPenguins, RDatasets
using MLJ, NearestNeighborModels, MLJScikitLearnInterface, MLJMultivariateStatsInterface
using MLJDecisionTreeInterface, MLJLinearModels
using CatBoost

InitError: Python: ModuleNotFoundError: No module named 'catboost'

Python stacktrace: none

during initialization of module CatBoost


Stacktrace:

  [1] pythrow()

    @ PythonCall ~/.julia/packages/PythonCall/wXfah/src/err.jl:94

  [2] errcheck

    @ PythonCall ~/.julia/packages/PythonCall/wXfah/src/err.jl:10 [inlined]

  [3] pyimport(m::String)

    @ PythonCall ~/.julia/packages/PythonCall/wXfah/src/concrete/import.jl:11

  [4] __init__()

    @ CatBoost ~/.julia/packages/CatBoost/TiqIz/src/CatBoost.jl:16

  [5] run_module_init(mod::Module, i::Int64)

    @ Base ./loading.jl:1128

  [6] register_restored_modules(sv::Core.SimpleVector, pkg::Base.PkgId, path::String)

    @ Base ./loading.jl:1116

  [7] _include_from_serialized(pkg::Base.PkgId, path::String, ocachepath::String, depmods::Vector{Any})

    @ Base ./loading.jl:1061

  [8] _require_search_from_serialized(pkg::Base.PkgId, sourcepath::String, build_id::UInt128)

    @ Base ./loading.jl:1575

  [9] _require(pkg::Base.PkgId, env::String)

    @ Base ./loading.jl:1932

 [10] __require_prelocked(uuidkey::Base.PkgId, env::String)

    @ Base ./loading.jl:1806

 [11] #invoke_in_world#3

    @ Base ./essentials.jl:921 [inlined]

 [12] invoke_in_world

    @ Base ./essentials.jl:918 [inlined]

 [13] _require_prelocked(uuidkey::Base.PkgId, env::String)

    @ Base ./loading.jl:1797

 [14] macro expansion

    @ Base ./loading.jl:1784 [inlined]

 [15] macro expansion

    @ Base ./lock.jl:267 [inlined]

 [16] __require(into::Module, mod::Symbol)

    @ Base ./loading.jl:1747

 [17] #invoke_in_world#3

    @ Base ./essentials.jl:921 [inlined]

 [18] invoke_in_world

    @ Base ./essentials.jl:918 [inlined]

 [19] require(into::Module, mod::Symbol)

    @ Base ./loading.jl:1740

 [20] eval

    @ ./boot.jl:385 [inlined]

 [21] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)

    @ Base ./loading.jl:2070

 [22] #invokelatest#2

    @ ./essentials.jl:887 [inlined]

 [23] invokelatest

    @ ./essentials.jl:884 [inlined]

 [24] (::VSCodeServer.var"#214#215"{VSCodeServer.NotebookRunCellArguments, String})()

    @ VSCodeServer ~/.vscode/extensions/julialang.language-julia-1.75.2/scripts/packages/VSCodeServer/src/serve_notebook.jl:19

 [25] withpath(f::VSCodeServer.var"#214#215"{VSCodeServer.NotebookRunCellArguments, String}, path::String)

    @ VSCodeServer ~/.vscode/extensions/julialang.language-julia-1.75.2/scripts/packages/VSCodeServer/src/repl.jl:274

 [26] notebook_runcell_request(conn::VSCodeServer.JSONRPC.JSONRPCEndpoint{Base.PipeEndpoint, Base.PipeEndpoint}, params::VSCodeServer.NotebookRunCellArguments)

    @ VSCodeServer ~/.vscode/extensions/julialang.language-julia-1.75.2/scripts/packages/VSCodeServer/src/serve_notebook.jl:13

 [27] dispatch_msg(x::VSCodeServer.JSONRPC.JSONRPCEndpoint{Base.PipeEndpoint, Base.PipeEndpoint}, dispatcher::VSCodeServer.JSONRPC.MsgDispatcher, msg::Dict{String, Any})

    @ VSCodeServer.JSONRPC ~/.vscode/extensions/julialang.language-julia-1.75.2/scripts/packages/JSONRPC/src/typed.jl:67

 [28] serve_notebook(pipename::String, outputchannel_logger::Base.CoreLogging.SimpleLogger; crashreporting_pipename::String)

    @ VSCodeServer ~/.vscode/extensions/julialang.language-julia-1.75.2/scripts/packages/VSCodeServer/src/serve_notebook.jl:139

 [29] top-level scope

    @ ~/.vscode/extensions/julialang.language-julia-1.75.2/scripts/notebook/notebook.jl:35


# Read the CSV file into a DataFrames
loan_data = CSV.read("Data/loan_data.csv", DataFrame)

# Display first few rows
first(loan_data, 10)


names(loan_data)

13-element Vector{String}:
 "Loan_ID"
 "Gender"
 "Married"
 "Dependents"
 "Education"
 "Self_Employed"
 "ApplicantIncome"
 "CoapplicantIncome"
 "LoanAmount"
 "Loan_Amount_Term"
 "Credit_History"
 "Property_Area"
 "Loan_Status"


# Get an overview of the Data
describe(loan_data)


# Check the size of the DataFrame
size(loan_data)

(381, 13)


# Remove rows with any missing values 
clean_data = dropmissing(loan_data)

# Check size again after cleaning
size(clean_data)

(308, 13)


# Check for missing values
for col in names(clean_data)
    missing_count = sum(ismissing, clean_data[!, col])
    println("Column: $col has $missing_count missing values")
end

Column: Loan_ID has 0 missing values
Column: Gender has 0 missing values
Column: Married has 0 missing values
Column: Dependents has 0 missing values
Column: Education has 0 missing values
Column: Self_Employed has 0 missing values
Column: ApplicantIncome has 0 missing values
Column: CoapplicantIncome has 0 missing values
Column: LoanAmount has 0 missing values
Column: Loan_Amount_Term has 0 missing values
Column: Credit_History has 0 missing values
Column: Property_Area has 0 missing values
Column: Loan_Status has 0 missing values


# Copy clean_data to a new DataFrame called real_data to preserve the original data
real_data = copy(clean_data)

# Transforming string variables into numeric variables
real_data[!, :Loan_Status] = ifelse.(real_data[!, :Loan_Status] .== "Y", 1, 0)
real_data[!, :Gender] = ifelse.(real_data[!, :Gender] .== "Female", 1, 0)
real_data[!, :Married] = ifelse.(real_data[!, :Married] .== "Yes", 1, 0)
real_data[!, :Education] = ifelse.(real_data[!, :Education] .== "Graduate", 1, 0)
real_data[!, :Self_Employed] = ifelse.(real_data[!, :Self_Employed] .== "Yes", 1, 0)

# Transform Property_Area into numeric variable with Urban -> 2, SemiUrban -> 1, Rural -> 0
real_data[!, :Property_Area] = map(x -> x == "Urban" ? 2 : x == "Semiurban" ? 1 : 0, real_data[!, :Property_Area])

# Replace "3+" with 3 in Dependents and convert to integer
real_data[!, :Dependents] = replace.(real_data[!, :Dependents], "3+" => "3")
real_data[!, :Dependents] = parse.(Int, real_data[!, :Dependents])

describe(real_data)


# Calculate the counts for each Loan_Status category
loan_status_counts = combine(groupby(clean_data, :Loan_Status), nrow => :count)

# Generate the bar plot
bar_plot = bar(loan_status_counts.Loan_Status, loan_status_counts.count, 
    xlabel = "Loan Status", 
    ylabel = "Count", 
    title = "Distribution of Loan Status",
    legend = false)

# Add the count annotations on top of each bar
for (x, y) in zip(loan_status_counts.Loan_Status, loan_status_counts.count)
    annotate!(bar_plot, [(x, y, text(string(y), 8, :center, :bottom))])
end

# Printing Output
bar_plot


# 2. Applicant Income By Loan Status
boxplot(real_data[!, :Loan_Status], real_data[!, :ApplicantIncome], 
    title = "Applicant Income by Loan Status", 
    ylabel = "Applicant Income",
    legend = false
    )


# 3. Gender Histogram
histogram(real_data[!, :Gender], bins = 2, title = "Gender Distribution", legend = false)


# 4. Married Histogram
histogram(real_data[!, :Married], bins = 2, title = "Married Status Distribution", legend = false)


# 5. Dependent Histogram
histogram(real_data[!, :Dependents], title = "Dependents Distribution", legend = false)


# 6. Education Histogram
histogram(real_data[!, :Education], bins = 2, title = "Education Distribution", legend = false)


# 7. Self_Employed Histogram
histogram(real_data[!, :Self_Employed], bins = 2, title = "Self Employed Distribution", legend = false)


# 8. ApplicantIncome Histogram
histogram(real_data[!, :ApplicantIncome], title = "Applicant Income Distribution", xlabel = "Income", ylabel = "Frequency", legend = false)


# 9. CoApplicantIncome Histogram
histogram(real_data[!, :CoapplicantIncome], title = "Co-Applicant Income Distribution", xlabel = "Income", ylabel = "Frequency", legend = false)


# 10. LoanAmount Histogram
loan_amount_hist = histogram(real_data[!, :LoanAmount], title = "Loan Amount Distribution", xlabel = "Amount", ylabel = "Frequency", legend = false)


# 11. Loan_Amount_Term Histogram
loan_amount_term_hist = histogram(real_data[!, :Loan_Amount_Term], title = "Loan Amount Term Distribution", xlabel = "Term", ylabel = "Frequency", legend = false)


# 12. Credit History Histogram
credit_history_hist = histogram(real_data[!, :Credit_History], bins = 2, title = "Credit History Distribution", legend = false)


# 13. Property Area Histogram
property_area_hist = histogram(real_data[!, :Property_Area], title = "Property Area Distribution", xlabel = "Area", ylabel = "Frequency", legend = false)


# 14. Property_Area vs Loan Amount Dot plot
xs = real_data[!, :Property_Area]
ys = real_data[!, :LoanAmount]

dotplot(xs, ys,
    title = "Property Area and Loan Amount Dot Plot",
    xlabel = "Property Area",
    ylabel = "Loan Amount",
    group = xs,
)


# 15. Property_Area vs 
xs = real_data[!, :Property_Area]
ys = real_data[!, :Loan_Amount_Term]

violin(xs, ys,
    title = "Property and Loan Amount Term Violin Plot",
    xlabel = "Property Area",
    ylabel = "Loan_Amount_Term",
    group = xs,
)


# 16. Correlation Plot Analysis part 1
selected_cols = [
    :Gender,
    :Married,
    :Education,
    :Dependents,
    :Self_Employed,
    :Property_Area,
    :Loan_Status
    ]
@df real_data corrplot(cols(selected_cols),
    size = (1000, 1000),
    bins = 32,
    title = "Correlation Plot",
    xlabel = "Variable",
    ylabel = "Variable",
    labelfontsize = 8,
    tickfontsize = 5,
    linewidth = 2,
    markersize = 10,
    clim = (-1, 1),
    colorbar_title = "Correlation Coefficient"
)


# 17. Correlation Plot Analysis part 2
selected_cols = [
    :ApplicantIncome,
    :LoanAmount,
    :Loan_Amount_Term,
    :Credit_History,
    :Loan_Status
    ]
@df real_data corrplot(cols(selected_cols),
    size = (1000, 1000),
    bins = 32,
    title = "Correlation Plot",
    xlabel = "Variable",
    ylabel = "Variable",
    labelfontsize = 8,
    tickfontsize = 5,
    linewidth = 2,
    markersize = 10,
    clim = (-1, 1),
    colorbar_title = "Correlation Coefficient"
)


# 18. Correlation Matrix for All Variables
M = cor(Matrix(real_data[!, 2:13]))

vars = names(real_data)[2:13]

fig = heatmap(M,
  title = "Correlation Matrix",
  clims = (-1, 1),
  xticks = (2:13, vars),
  yticks = (2:13, vars),
  color = cgrad(:balance, rev = true),
  xrot = 45,
  aspect = :ratio,
  size = (700, 600),
)

for j in axes(M, 2), i in axes(M, 1)
  annotate!(i, j, text("$(round(M[i,j], digits = 2))", :white, 12))
end

fig


select!(real_data, Not(:Loan_ID))


hot = MLJ.fit!(machine(OneHotEncoder(), real_data))

# apply the dummy coding scheme; note that we qualify `transform`
data_hot_encoded = MLJ.transform(hot, real_data)

# check
schema(data_hot_encoded)

┌ Info: Training machine(OneHotEncoder(features = Symbol[], …), …).
└ @ MLJBase /Users/nathanielzhu/.julia/packages/MLJBase/mIaqI/src/machines.jl:493

┌───────────────────┬────────────┬─────────┐
│ names             │ scitypes   │ types   │
├───────────────────┼────────────┼─────────┤
│ Gender            │ Count      │ Int64   │
│ Married           │ Count      │ Int64   │
│ Dependents        │ Count      │ Int64   │
│ Education         │ Count      │ Int64   │
│ Self_Employed     │ Count      │ Int64   │
│ ApplicantIncome   │ Count      │ Int64   │
│ CoapplicantIncome │ Continuous │ Float64 │
│ LoanAmount        │ Continuous │ Float64 │
│ Loan_Amount_Term  │ Continuous │ Float64 │
│ Credit_History    │ Continuous │ Float64 │
│ Property_Area     │ Count      │ Int64   │
│ Loan_Status       │ Count      │ Int64   │
└───────────────────┴────────────┴─────────┘


rng = StableRNG(1997)

train, test = partition(data_hot_encoded, 0.7; rng = rng, shuffle = true)

(216×12 DataFrame
 Row │ Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  ⋯
     │ Int64   Int64    Int64       Int64      Int64          Int64            ⋯
─────┼──────────────────────────────────────────────────────────────────────────
   1 │      0        0           0          0              0             2333  ⋯
   2 │      1        1           1          1              0             4608
   3 │      0        0           0          1              1             7167
   4 │      1        0           0          1              0             3159
   5 │      1        0           1          0              0             4606  ⋯
   6 │      0        1           0          1              0             3232
   7 │      0        1           2          1              0             5935
   8 │      1        0           0          0              0             3400
  ⋮  │   ⋮        ⋮         ⋮           ⋮            ⋮               ⋮         ⋱
 210 │      0        1           3          1              0             3400  ⋯
 211 │      0        1           0          0              0             3814
 212 │      0        1           2          1              0             4400
 213 │      0        1           0          1              0             5488
 214 │      1        0           0          1              0             1811  ⋯
 215 │      0        1           1          0              0             3500
 216 │      1        0           0          1              0             5000
                                                  6 columns and 201 rows omitted, 92×12 DataFrame
 Row │ Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  ⋯
     │ Int64   Int64    Int64       Int64      Int64          Int64            ⋯
─────┼──────────────────────────────────────────────────────────────────────────
   1 │      0        0           0          1              0             6277  ⋯
   2 │      0        1           3          0              1             7100
   3 │      0        0           0          1              0             2500
   4 │      0        1           1          1              0             2750
   5 │      1        1           0          1              0             2484  ⋯
   6 │      0        1           0          1              0             3246
   7 │      0        0           0          1              0             6000
   8 │      0        1           0          1              0             2958
  ⋮  │   ⋮        ⋮         ⋮           ⋮            ⋮               ⋮         ⋱
  86 │      1        0           0          1              0             3762  ⋯
  87 │      0        1           0          1              0             4860
  88 │      0        1           0          1              0             3597
  89 │      1        0           1          1              1             8624
  90 │      0        1           1          1              0             2882  ⋯
  91 │      1        1           0          0              1             7142
  92 │      0        1           3          1              0             8750
                                                   6 columns and 77 rows omitted)


y = categorical((real_data[!, :Loan_Status]), levels = [0,1])
X = select(real_data, Not([:Loan_Status]))
levels(y)

2-element Vector{Int64}:
 0
 1


train, test = partition(eachindex(y), 0.8, shuffle=true, rng=1997);


# checking best models for our work

for m in models(matching(X, y))
    println("""
    [Model: $(m.name)]
    \t prediction type: $(m.prediction_type)
    \t source package:  $(m.package_name)
    """)
end

[Model: AdaBoostStumpClassifier]
	 prediction type: probabilistic
	 source package:  DecisionTree

[Model: CatBoostClassifier]
	 prediction type: probabilistic
	 source package:  CatBoost

[Model: ConstantClassifier]
	 prediction type: probabilistic
	 source package:  MLJModels

[Model: DecisionTreeClassifier]
	 prediction type: probabilistic
	 source package:  BetaML

[Model: DecisionTreeClassifier]
	 prediction type: probabilistic
	 source package:  DecisionTree

[Model: DeterministicConstantClassifier]
	 prediction type: deterministic
	 source package:  MLJModels

[Model: EvoTreeClassifier]
	 prediction type: probabilistic
	 source package:  EvoTrees

[Model: KernelPerceptronClassifier]
	 prediction type: probabilistic
	 source package:  BetaML

[Model: NeuralNetworkClassifier]
	 prediction type: probabilistic
	 source package:  BetaML

[Model: PegasosClassifier]
	 prediction type: probabilistic
	 source package:  BetaML

[Model: PerceptronClassifier]
	 prediction type: probabilistic
	 source package:  BetaML

[Model: RandomForestClassifier]
	 prediction type: probabilistic
	 source package:  BetaML

[Model: RandomForestClassifier]
	 prediction type: probabilistic
	 source package:  DecisionTree

[Model: RandomForestClassifier]
	 prediction type: probabilistic
	 source package:  MLJScikitLearnInterface

[Model: StableForestClassifier]
	 prediction type: probabilistic
	 source package:  SIRUS

[Model: StableRulesClassifier]
	 prediction type: probabilistic
	 source package:  SIRUS


KNNClassifier = @load KNNClassifier verbosity = 0
LDA = @load LDA verbosity = 0
NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = MLJFlux verbosity = 0
MultinomialClassifier = @load MultinomialClassifier verbosity = 0
CatBoostClassifier = @load CatBoostClassifier verbosity = 0 pkg = CatBoost verbosity = 0
RandomForestClassifier = @load RandomForestClassifier pkg=BetaML verbosity = 0
DecisionTreeClassifier = @load DecisionTreeClassifier pkg = DecisionTree verbosity = 0

model_list = [
    KNNClassifier(K = 5), # use nearest 5-neighbors to make predictions
    LDA(),
    NeuralNetworkClassifier(),
    MultinomialClassifier(),
    CatBoostClassifier(),
    RandomForestClassifier(),
    DecisionTreeClassifier()
]

7-element Vector{Probabilistic}:
 KNNClassifier(K = 5, …)
 LDA(method = gevd, …)
 NeuralNetworkClassifier(builder = Short(n_hidden = 0, …), …)
 MultinomialClassifier(lambda = 2.220446049250313e-16, …)
 CatBoostClassifier(iterations = 1000, …)
 RandomForestClassifier(n_trees = 30, …)
 DecisionTreeClassifier(max_depth = -1, …)


acc = Float64[] # accuracy()
pre = Float64[] # multiclass_precision()
rec = Float64[] # multiclass_recall()
f1s = Float64[] # f1score()
mat = []        # confusion_matrix()

Any[]


for clf in model_list
    # Create a pipeline model that standardizes, then fits a classifier.
    model = Pipeline(Standardizer(), clf)

    # Fit the model onto the training set
    mach = machine(model, X, y)
    MLJ.fit!(mach, rows = train, verbosity = 0)

    # Make predictions on the test set
    yhat = MLJ.predict(mach, rows = test)

    # Evaluate the model on the test set using selected metrics
    #
    # NOTES:
    #
    #   - MLJ.predict() may give probabilistic predictions. Use mode() to collapse to a concrete target.
    #   - An evaluation metric F() always accepts inputs as F(fitted, observed).
    
    push!(acc, accuracy(mode.(yhat), y[test]))
    push!(pre, multiclass_precision(mode.(yhat), y[test]))
    push!(rec, multiclass_recall(mode.(yhat), y[test]))
    push!(f1s, f1score(mode.(yhat), y[test]))
    push!(mat, ConfusionMatrix(levels = levels(y))(mode.(yhat), y[test]))
    
end

┌ Warning: Levels not explicitly ordered. Using the order CategoricalValue{Int64, UInt32}[0, 1]. The "positive" level is 1. 
└ @ StatisticalMeasures.ConfusionMatrices /Users/nathanielzhu/.julia/packages/StatisticalMeasures/hPDX2/src/confusion_matrices.jl:339
┌ Warning: Levels not explicitly ordered. Using the order CategoricalValue{Int64, UInt32}[0, 1]. The "positive" level is 1. 
└ @ StatisticalMeasures.ConfusionMatrices /Users/nathanielzhu/.julia/packages/StatisticalMeasures/hPDX2/src/confusion_matrices.jl:339
┌ Warning: Layer with Float32 parameters got Float64 input.
│   The input will be converted, but any earlier layers may be very slow.
│   layer = Dense(11 => 5, σ)
│   summary(x) = 11×1 Matrix{Float64}
└ @ Flux /Users/nathanielzhu/.julia/packages/Flux/vzwqj/src/layers/stateless.jl:60
┌ Warning: Levels not explicitly ordered. Using the order CategoricalValue{Int64, UInt32}[0, 1]. The "positive" level is 1. 
└ @ StatisticalMeasures.ConfusionMatrices /Users/nathanielzhu/.julia/packages/StatisticalMeasures/hPDX2/src/confusion_matrices.jl:339


results = DataFrame(
    Model = typeof.(model_list),
    Accuracy = acc,
    Precision = pre,
    Recall = rec,
    F1 = f1s
)

UndefVarError: `model_list` not defined


Stacktrace:

 [1] top-level scope

   @ ~/Desktop/UCR/STAT206/Coding/Project/Project_Code.ipynb:1


@show mat[2]


glm_df = DataFrame(copy(real_data))

glm_df[!, :Credit_History] = CategoricalArray(glm_df[!, :Credit_History])
glm_df[!, :Property_Area] = CategoricalArray(glm_df[!, :Property_Area])
glm_df[!, :Loan_Status] = CategoricalArray(glm_df[!, :Loan_Status])
glm_df[!, :Education] = CategoricalArray(glm_df[!, :Education])
glm_df[!, :Gender] = CategoricalArray(glm_df[!, :Gender])
glm_df[!, :Self_Employed] = CategoricalArray(glm_df[!, :Self_Employed])

# Define the formula for logistic regression
formula = @formula(Loan_Status ~ ApplicantIncome + LoanAmount + Loan_Amount_Term + Credit_History + Property_Area)

# Fit the logistic regression model
# model = lm(@formula(Loan_Status ~ ApplicantIncome + LoanAmount + Loan_Amount_Term + Credit_History + Property_Area), glm_df)

probit = glm(@formula(Loan_Status ~ ApplicantIncome + LoanAmount + Loan_Amount_Term + Credit_History + Property_Area), glm_df, Binomial(), ProbitLink())

# Print the model summary
println(model)

Row	Gender	Married	Dependents	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Property_Area	Loan_Status
	Int64	Int64	Int64	Int64	Int64	Int64	Float64	Float64	Float64	Float64	Int64	Int64
1	0	1	1	1	0	4583	1508.0	128.0	360.0	1.0	0	0
2	0	1	0	1	1	3000	0.0	66.0	360.0	1.0	2	1
3	0	1	0	0	0	2583	2358.0	120.0	360.0	1.0	2	1
4	0	0	0	1	0	6000	0.0	141.0	360.0	1.0	2	1
5	0	1	0	0	0	2333	1516.0	95.0	360.0	1.0	2	1
6	0	1	2	1	0	3200	700.0	70.0	360.0	1.0	2	1
7	0	0	0	1	0	1853	2840.0	114.0	360.0	1.0	0	0
8	0	1	2	1	0	1299	1086.0	17.0	120.0	1.0	2	1
9	0	0	0	1	0	4950	0.0	125.0	360.0	1.0	2	1
10	1	0	0	1	0	3510	0.0	76.0	360.0	0.0	2	0
11	0	1	0	0	0	4887	0.0	133.0	360.0	1.0	0	0
12	0	1	0	0	0	7660	0.0	104.0	360.0	0.0	2	0
13	0	1	0	0	0	2600	1911.0	116.0	360.0	0.0	1	0
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
297	0	1	1	1	0	2787	1917.0	146.0	360.0	0.0	0	0
298	0	1	0	1	0	2297	1522.0	104.0	360.0	1.0	2	1
299	1	0	0	0	0	2165	0.0	70.0	360.0	1.0	1	1
300	0	1	2	1	1	2726	0.0	106.0	360.0	0.0	1	0
301	0	1	0	1	0	3000	3416.0	56.0	180.0	1.0	1	1
302	0	1	0	1	0	3859	3300.0	142.0	180.0	1.0	0	1
303	0	0	0	0	0	3833	0.0	110.0	360.0	1.0	0	1
304	0	1	3	1	0	5703	0.0	128.0	360.0	1.0	2	1
305	0	1	0	1	0	3232	1950.0	108.0	360.0	1.0	0	1
306	1	0	0	1	0	2900	0.0	71.0	360.0	1.0	0	1
307	0	1	3	1	0	4106	0.0	40.0	180.0	1.0	0	1
308	1	0	0	1	1	4583	0.0	133.0	360.0	0.0	1	0

STAT 206 Final Project¶

Packages¶

Main Coding¶

Part 1: Data Importing and Cleaning¶

Part 2: Data Visualization¶

Data Modeling Starts Here¶

Row	Loan_ID	Gender	Married	Dependents	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Property_Area	Loan_Status
	String15	String7?	String3	String3?	String15	String3?	Int64	Float64	Float64	Float64?	Float64?	String15	String1
1	LP001003	Male	Yes	1	Graduate	No	4583	1508.0	128.0	360.0	1.0	Rural	N
2	LP001005	Male	Yes	0	Graduate	Yes	3000	0.0	66.0	360.0	1.0	Urban	Y
3	LP001006	Male	Yes	0	Not Graduate	No	2583	2358.0	120.0	360.0	1.0	Urban	Y
4	LP001008	Male	No	0	Graduate	No	6000	0.0	141.0	360.0	1.0	Urban	Y
5	LP001013	Male	Yes	0	Not Graduate	No	2333	1516.0	95.0	360.0	1.0	Urban	Y
6	LP001024	Male	Yes	2	Graduate	No	3200	700.0	70.0	360.0	1.0	Urban	Y
7	LP001027	Male	Yes	2	Graduate	missing	2500	1840.0	109.0	360.0	1.0	Urban	Y
8	LP001029	Male	No	0	Graduate	No	1853	2840.0	114.0	360.0	1.0	Rural	N
9	LP001030	Male	Yes	2	Graduate	No	1299	1086.0	17.0	120.0	1.0	Urban	Y
10	LP001032	Male	No	0	Graduate	No	4950	0.0	125.0	360.0	1.0	Urban	Y

Row	variable	mean	min	median	max	nmissing	eltype
	Symbol	Union…	Any	Union…	Any	Int64	Type
1	Loan_ID		LP001003		LP002990	0	String15
2	Gender		Female		Male	5	Union{Missing, String7}
3	Married		No		Yes	0	String3
4	Dependents		0		3+	8	Union{Missing, String3}
5	Education		Graduate		Not Graduate	0	String15
6	Self_Employed		No		Yes	21	Union{Missing, String3}
7	ApplicantIncome	3579.85	150	3333.0	9703	0	Int64
8	CoapplicantIncome	1277.28	0.0	983.0	33837.0	0	Float64
9	LoanAmount	104.987	9.0	110.0	150.0	0	Float64
10	Loan_Amount_Term	340.865	12.0	360.0	480.0	11	Union{Missing, Float64}
11	Credit_History	0.837607	0.0	1.0	1.0	30	Union{Missing, Float64}
12	Property_Area		Rural		Urban	0	String15
13	Loan_Status		N		Y	0	String1